Chess engine: Stockfish 21051818

Stockfish - UCI chess engine, compiled by Tomasz Sobczyk
Rating JCER=3144

Timestamp: 1621353983

New NNUE architecture and net

Introduces a new NNUE network architecture and associated network parameters,

as obtained by a new pytorch trainer.

The network is already very strong at short TC, without regression at longer TC,

and has potential for further improvements.

https://tests.stockfishchess.org/tests/view/60a159c65085663412d0921d

TC: 10s+0.1s, 1 thread

ELO: 21.74 +-3.4 (95%) LOS: 100.0%

Total: 10000 W: 1559 L: 934 D: 7507 Elo +21.74

Ptnml(0-2): 38, 701, 2972, 1176, 113

https://tests.stockfishchess.org/tests/view/60a187005085663412d0925b

TC: 60s+0.6s, 1 thread

ELO: 5.85 +-1.7 (95%) LOS: 100.0%

Total: 20000 W: 1381 L: 1044 D: 17575 Elo +5.85

Ptnml(0-2): 27, 885, 7864, 1172, 52

https://tests.stockfishchess.org/tests/view/60a2beede229097940a03806

TC: 20s+0.2s, 8 threads

LLR: 2.93 (-2.94,2.94) <0.50,3.50>

Total: 34272 W: 1610 L: 1452 D: 31210 Elo +1.60

Ptnml(0-2): 30, 1285, 14350, 1439, 32

https://tests.stockfishchess.org/tests/view/60a2d687e229097940a03c72

TC: 60s+0.6s, 8 threads

LLR: 2.94 (-2.94,2.94) <-2.50,0.50>

Total: 45544 W: 1262 L: 1214 D: 43068 Elo +0.37

Ptnml(0-2): 12, 1129, 20442, 1177, 12

The network has been trained (by vondele) using the https://github.com/glinscott/nnue-pytorch/ trainer (started by glinscott),

specifically the branch https://github.com/Sopel97/nnue-pytorch/tree/experiment_56.

The data used are in 64 billion positions (193GB total) generated and scored with the current master net

d8: https://drive.google.com/file/d/1hOOYSDKgOOp38ZmD0N4DV82TOLHzjUiF/view?usp=sharing

d9: https://drive.google.com/file/d/1VlhnHL8f-20AXhGkILujnNXHwy9T-MQw/view?usp=sharing

d10: https://drive.google.com/file/d/1ZC5upzBYMmMj1gMYCkt6rCxQG0GnO3Kk/view?usp=sharing

fishtest_d9: https://drive.google.com/file/d/1GQHt0oNgKaHazwJFTRbXhlCN3FbUedFq/view?usp=sharing

This network also contains a few architectural changes with respect to the current master:

Size changed from 256x2-32-32-1 to 512x2-16-32-1

~15-20% slower

~2x larger

adds a special path for 16 valued ClippedReLU

fixes affine transform code for 16 inputs/outputs, buy using InputDimensions instead of PaddedInputDimensions

this is safe now because the inputs are processed in groups of 4 in the current affine transform code

The feature set changed from HalfKP to HalfKAv2

Includes information about the kings like HalfKA

Packs king features better, resulting in 8% size reduction compared to HalfKA

The board is flipped for the black's perspective, instead of rotated like in the current master

PSQT values for each feature

the feature transformer now outputs a part that is fowarded directly to the output and allows learning piece values more directly than the previous network architecture. The effect is visible for high imbalance positions, where the current master network outputs evaluations skewed towards zero.

8 PSQT values per feature, chosen based on (popcount(pos.pieces()) - 1) / 4

initialized to classical material values on the start of the training

8 subnetworks (512x2->16->32->1), chosen based on (popcount(pos.pieces()) - 1) / 4

only one subnetwork is evaluated for any position, no or marginal speed loss

A diagram of the network is available: https://user-images.githubusercontent.com/8037982/118656988-553a1700-b7eb-11eb-82ef-56a11cbebbf2.png

A more complete description: https://github.com/glinscott/nnue-pytorch/blob/master/docs/nnue.md

Bench: 3806488

Stockfish 21051818 and NNUE download